Unmasking the benefits of donepezil via psychometrically precise identification of mild cognitive impairment: A secondary analysis of the ADCS vitamin E and donepezil in MCI study.

INTRODUCTION: Criteria for mild cognitive impairment (MCI) used in many clinical trials are susceptible to "false-positive (FP)" errors that can be avoided by an actuarial psychometric approach. METHODS: Cluster analysis was applied to baseline neuropsychological test data from 756 MCI participants in the Alzheimer's Disease Cooperative Study donepezil trial. Treatment groups were compared after FP MCI cases were removed. RESULTS: Cluster analyses revealed three groups: "single-domain amnestic MCI" (31%), "multi-domain amnestic MCI" (39%), and "FP MCI" (30%). After removing FP MCI cases, the donepezil treatment group had a lower rate of progression to Alzheimer's disease and better performance on cognitive tests than the placebo/vitamin E group. DISCUSSION: Removal of FP MCI diagnoses unmasked beneficial effects of donepezil, despite a 30% reduction in sample size. MCI subject selection based on actuarial methods with comprehensive neuropsychological test data can result in more efficient clinical trials and improved ability to detect treatment effects.

Potential implications of practice effects in Alzheimer's disease prevention trials.

INTRODUCTION: Practice effects (PEs) present a potential confound in clinical trials with cognitive outcomes. A single-blind placebo run-in design, with repeated cognitive outcome assessments before randomization to treatment, can minimize effects of practice on trial outcome. METHODS: We investigated the potential implications of PEs in Alzheimer's disease prevention trials using placebo arm data from the Alzheimer's Disease Cooperative Study donepezil/vitamin E trial in mild cognitive impairment. Frequent ADAS-Cog measurements early in the trial allowed us to compare two competing trial designs: a 19-month trial with randomization after initial assessment, versus a 15-month trial with a 4-month single-blind placebo run-in and randomization after the second administration of the ADAS-Cog. Standard power calculations assuming a mixed-model repeated-measure analysis plan were used to calculate sample size requirements for a hypothetical future trial designed to detect a 50% slowing of cognitive decline. RESULTS: On average, ADAS-Cog 13 scores improved at first follow-up, consistent with a PE and progressively worsened thereafter. The observed change for a 19-month trial (1.18 points) was substantively smaller than that for a 15-month trial with 4-month run-in (1.79 points). To detect a 50% slowing in progression under the standard design (i.e., a 0.59 point slowing), a future trial would require 3.4 times more subjects than would be required to detect the comparable percent slowing (i.e., 0.90 points) with the run-in design. DISCUSSION: Assuming the improvement at first follow-up observed in this trial represents PEs, the rate of change from the second assessment forward is a more accurate representation of symptom progression in this population and is the appropriate reference point for describing treatment effects characterized as percent slowing of symptom progression; failure to accommodate this leads to an oversized clinical trial. We conclude that PEs are an important potential consideration when planning future trials.

Design of pilot studies to inform the construction of composite outcome measures.

BACKGROUND: Composite scales have recently been proposed as outcome measures for clinical trials. For example, the Prodromal Alzheimer's Cognitive Composite (PACC) is the sum of z-score normed component measures assessing episodic memory, timed executive function, and global cognition. Alternative methods of calculating composite total scores using the weighted sum of the component measures that maximize signal-to-noise of the resulting composite score have been proposed. Optimal weights can be estimated from pilot data, but it is an open question how large a pilot trial is required to calculate reliably optimal weights. METHODS: In this manuscript, we describe the calculation of optimal weights, and use large-scale computer simulations to investigate the question of how large a pilot study sample is required to inform the calculation of optimal weights. The simulations are informed by the pattern of decline observed in cognitively normal subjects enrolled in the Alzheimer's Disease Cooperative Study (ADCS) Prevention Instrument cohort study, restricting to n=75 subjects age 75 and over with an ApoE E4 risk allele and therefore likely to have an underlying Alzheimer neurodegenerative process. RESULTS: In the context of secondary prevention trials in Alzheimer's disease, and using the components of the PACC, we found that pilot studies as small as 100 are sufficient to meaningfully inform weighting parameters. Regardless of the pilot study sample size used to inform weights, the optimally weighted PACC consistently outperformed the standard PACC in terms of statistical power to detect treatment effects in a clinical trial. Pilot studies of size 300 produced weights that achieved near-optimal statistical power, and reduced required sample size relative to the standard PACC by more than half. CONCLUSIONS: These simulations suggest that modestly sized pilot studies, comparable to that of a phase 2 clinical trial, are sufficient to inform the construction of composite outcome measures. Although these findings apply only to the PACC in the context of prodromal AD, the observation that weights only have to approximate the optimal weights to achieve near-optimal performance should generalize. Performing a pilot study or phase 2 trial to inform the weighting of proposed composite outcome measures is highly cost-effective. The net effect of more efficient outcome measures is that smaller trials will be required to test novel treatments. Alternatively, second generation trials can use prior clinical trial data to inform weighting, so that greater efficiency can be achieved as we move forward.

Association between Cholesterol Exposure and Neuropathological Findings: The ACT Study.

We characterized the relationship between late life cholesterol exposure and neuropathological outcomes in a community-based, older adult cohort. Adult Changes in Thought (ACT) is a cohort study that enrolls consenting, randomly selected, non-demented people aged ≥65 from a healthcare delivery system. We used late life HDL and total cholesterol lab values from Group Health computerized records, and calculated HDL and non-HDL levels. We evaluated neuropathological outcomes of Alzheimer's disease, cerebral amyloid angiopathy, vascular brain injury, and Lewy body disease. Using linear mixed models with age and antilipemic medication as predictors, we obtained predicted cholesterol values at age 70 and 10 years prior to death for individuals with available cholesterol data in 10-year exposure windows. We used logistic regression to determine whether predicted late life cholesterol levels were associated with neuropathological outcomes controlling for age at death, APOE genotype, sex, and their interactions with cholesterol levels. 525 decedents came to autopsy by 08/2014. Of these, plasma cholesterol concentration was available for 318 (age 70, model 1) and 396 (10 years prior to death, model 2) participants. We did not find associations between late life cholesterol and Alzheimer's disease neuropathological changes, and there were no associations between cholesterol levels and amyloid angiopathy or vascular brain injury. We observed an association between predicted non-HDL cholesterol at age 70 and Lewy body disease. Our study suggests an association between late life non-HDL cholesterol exposure and Lewy body disease. We did not observe associations between late life cholesterol levels and Braak stage or CERAD score.

A human neurodevelopmental model for Williams syndrome.

Williams syndrome is a genetic neurodevelopmental disorder characterized by an uncommon hypersociability and a mosaic of retained and compromised linguistic and cognitive abilities. Nearly all clinically diagnosed individuals with Williams syndrome lack precisely the same set of genes, with breakpoints in chromosome band 7q11.23 (refs 1-5). The contribution of specific genes to the neuroanatomical and functional alterations, leading to behavioural pathologies in humans, remains largely unexplored. Here we investigate neural progenitor cells and cortical neurons derived from Williams syndrome and typically developing induced pluripotent stem cells. Neural progenitor cells in Williams syndrome have an increased doubling time and apoptosis compared with typically developing neural progenitor cells. Using an individual with atypical Williams syndrome, we narrowed this cellular phenotype to a single gene candidate, frizzled 9 (FZD9). At the neuronal stage, layer V/VI cortical neurons derived from Williams syndrome were characterized by longer total dendrites, increased numbers of spines and synapses, aberrant calcium oscillation and altered network connectivity. Morphometric alterations observed in neurons from Williams syndrome were validated after Golgi staining of post-mortem layer V/VI cortical neurons. This model of human induced pluripotent stem cells fills the current knowledge gap in the cellular biology of Williams syndrome and could lead to further insights into the molecular mechanism underlying the disorder and the human social brain.

Gain of Toxicity from ALS/FTD-Linked Repeat Expansions in C9ORF72 Is Alleviated by Antisense Oligonucleotides Targeting GGGGCC-Containing RNAs.

Hexanucleotide expansions in C9ORF72 are the most frequent genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Disease mechanisms were evaluated in mice expressing C9ORF72 RNAs with up to 450 GGGGCC repeats or with one or both C9orf72 alleles inactivated. Chronic 50% reduction of C9ORF72 did not provoke disease, while its absence produced splenomegaly, enlarged lymph nodes, and mild social interaction deficits, but not motor dysfunction. Hexanucleotide expansions caused age-, repeat-length-, and expression-level-dependent accumulation of RNA foci and dipeptide-repeat proteins synthesized by AUG-independent translation, accompanied by loss of hippocampal neurons, increased anxiety, and impaired cognitive function. Single-dose injection of antisense oligonucleotides (ASOs) that target repeat-containing RNAs but preserve levels of mRNAs encoding C9ORF72 produced sustained reductions in RNA foci and dipeptide-repeat proteins, and ameliorated behavioral deficits. These efforts identify gain of toxicity as a central disease mechanism caused by repeat-expanded C9ORF72 and establish the feasibility of ASO-mediated therapy.

Proof of concept demonstration of optimal composite MRI endpoints for clinical trials.

BACKGROUND: Atrophy measures derived from structural MRI are promising outcome measures for early phase clinical trials, especially for rare diseases such as primary progressive aphasia (PPA), where the small available subject pool limits our ability to perform meaningfully powered trials with traditional cognitive and functional outcome measures. METHODS: We investigated a composite atrophy index in 26 PPA participants with longitudinal MRIs separated by two years. Rogalski et al. [Neurology 2014;83:1184-1191] previously demonstrated that atrophy of the left perisylvian temporal cortex (PSTC) is a highly sensitive measure of disease progression in this population and a promising endpoint for clinical trials. Using methods described by Ard et al. [Pharmaceutical Statistics 2015;14:418-426], we constructed a composite atrophy index composed of a weighted sum of volumetric measures of 10 regions of interest within the left perisylvian cortex using weights that maximize signal-to-noise and minimize sample size required of trials using the resulting score. Sample size required to detect a fixed percentage slowing in atrophy in a two-year clinical trial with equal allocation of subjects across arms and 90% power was calculated for the PSTC and optimal composite surrogate biomarker endpoints. RESULTS: The optimal composite endpoint required 38% fewer subjects to detect the same percent slowing in atrophy than required by the left PSTC endpoint. CONCLUSIONS: Optimal composites can increase the power of clinical trials and increase the probability that smaller trials are informative, an observation especially relevant for PPA, but also for related neurodegenerative disorders including Alzheimer's disease.

Optimal composite scores for longitudinal clinical trials under the linear mixed effects model.

Clinical trials of chronic, progressive conditions use rate of change on continuous measures as the primary outcome measure, with slowing of progression on the measure as evidence of clinical efficacy. For clinical trials with a single prespecified primary endpoint, it is important to choose an endpoint with the best signal-to-noise properties to optimize statistical power to detect a treatment effect. Composite endpoints composed of a linear weighted average of candidate outcome measures have also been proposed. Composites constructed as simple sums or averages of component tests, as well as composites constructed using weights derived from more sophisticated approaches, can be suboptimal, in some cases performing worse than individual outcome measures. We extend recent research on the construction of efficient linearly weighted composites by establishing the often overlooked connection between trial design and composite performance under linear mixed effects model assumptions and derive a formula for calculating composites that are optimal for longitudinal clinical trials of known, arbitrary design. Using data from a completed trial, we provide example calculations showing that the optimally weighted linear combination of scales can improve the efficiency of trials by almost 20% compared with the most efficient of the individual component scales. Additional simulations and analytical results demonstrate the potential losses in efficiency that can result from alternative published approaches to composite construction and explore the impact of weight estimation on composite performance.

Improved statistical power of Alzheimer clinical trials by item-response theory: proof of concept by application to the activities of daily living scale.

Discovery of effective treatment for Alzheimer disease (AD) depends upon the availability of outcome measures that exhibit good sensitivity to rates of longitudinal decline on global functional performance. The Alzheimer's Disease Cooperative Study-Activities of Daily Living inventory (ADCS-ADL) is a frequently used functional endpoint in clinical trials for AD that assesses patient functional ability on the basis of informant ratings of patient performance on a variety of everyday tasks. Previous research has shown that the items comprising the ADCS-ADL are sensitive to characteristic longitudinal trajectories in AD. However, standard procedures for combining information from individual items into an overall test score may not make full use of the information provided by informant responses. The current study explored an application of item-response theory (IRT) techniques to the calculation of test scores on the ADCS-ADL. Using data from 2 ADCS clinical trials on mild-to-moderate AD patients we found that IRT based scoring increased sensitivity to change in functional ability and improved prospective statistical power of the ADCS-ADL as an outcome measure in clinical trials.

Power Calculations for Two-Wave, Change from Baseline to Follow-up Study Designs.

Change in a quantitative trait is commonly employed as an endpoint in two-wave longitudinal studies. For example, early phase clinical trials often use two-wave designs with biomarker endpoints to confirm that a treatment affects the putative target treatment pathway before proceeding to larger scale clinical efficacy trials. Power calculations for such designs are straightforward if pilot data from longitudinal investigations of similar duration to the proposed study are available. Often longitudinal pilot data of similar duration are not available, and simplifying assumptions are used to calculate sample size from cross-sectional data, one standard approach being to use a formula based on variance estimated from cross sectional data and correlation estimates abstracted from the literature or inferred from experience with similar endpoints. An implicit assumption of this standard approach is that the variance of the quantitative trait is the same at baseline and follow-up. In practice, this assumption rarely holds, and sample size estimates by this standard formula can be dramatically anti-conservative. Even when longitudinal pilot data for estimating parameters required in sample size calculations are available, sample size calculations will be biased if the interval from baseline to follow-up is not of similar duration to that proposed for the study being designed. In this paper we characterize the magnitude of bias in sample size estimates when formula assumptions do not hold and derive alternative conservative formulas for sample size required to achieve nominal power.

Power calculations for clinical trials in Alzheimer's disease.

The Alzheimer research community is actively pursuing novel biomarker and other biologic measures to characterize disease progression or to use as outcome measures in clinical trials. One product of these efforts has been a large literature reporting power calculations and estimates of sample size for planning future clinical trials and cohort studies with longitudinal rate of change outcome measures. Sample size estimates reported in this literature vary greatly depending on a variety of factors, including the statistical methods and model assumptions used in their calculation. We review this literature and suggest standards for reporting power calculation results. Regardless of the statistical methods used, studies consistently find that volumetric neuroimaging measures of regions of interest, such as hippocampal volume, outperform global cognitive scales traditionally used in clinical treatment trials in terms of the number of subjects required to detect a fixed percentage slowing of the rate of change observed in demented and cognitively impaired populations. However, statistical methods, model assumptions, and parameter estimates used in power calculations are often not reported in sufficient detail to be of maximum utility. We review the factors that influence sample size estimates, and discuss outstanding issues relevant to planning longitudinal studies of Alzheimer's disease.

Sleep does not enhance motor sequence learning.

Improvements in motor sequence performance have been observed after a delay involving sleep. This finding has been taken as evidence for an active sleep consolidation process that enhances subsequent performance. In a review of this literature, however, the authors observed 4 aspects of data analyses and experimental design that could lead to improved performance on the test in the absence of any sleep consolidation: (a) masking of learning effects in the averaged data, (b) masking of reactive inhibition effects in the averaged training data, (c) time-of-day and time-since-sleep confounds, and (d) a gradual buildup of fatigue over the course of massed (i.e., concentrated) training. In 2 experiments the authors show that when these factors are controlled for, or when their effects are substantially reduced, the sleep enhancement effect is eliminated. Whereas sleep may play a role in protection from forgetting of motor skills, it does not result in performance enhancement.